Development of a Lightweight Convolutional Model for Leaf Disease Identification
Abstract
Agricultural production faces significant annual losses due to plant diseases, with economic impacts exceeding 40 million dollars and contributing to acute hunger affecting over 281.6 million people in 2023. The identification of plant diseases through leaf image analysis is crucial to mitigate these losses. This study proposes a lightweight Convolutional Neural Network model, inspired by the MobileNet architecture, to classify various plant leaf diseases. Utilizing a public dataset, this research aims to develop a model that balances performance and computational efficiency. The proposed model achieved an accuracy of 98.94%, with precision, recall, F1-Score, and AUC metrics of 98.48%, 98.38%, 98.41%, and 99.99%, respectively. A comparative analysis was conducted with MobileNetV2 and a reference model from previous research, demonstrating the effectiveness of the proposed architecture in maintaining high performance with a reduced number of parameters.
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